Content based adjustment of an image

ABSTRACT

A video input signal is analyzed to detect image content and image properties, wherein detecting the image content includes automatically deriving image features. A content group is determined for the video input signal based on the detected image content, the content group including predefined image properties. The image properties of the video input signal are adjusted based on a difference between the detected image properties and the predefined image properties.

FIELD OF THE INVENTION

This invention relates generally to adjusting a video signal, and moreparticularly to adjusting image display settings based on signalproperties, image properties and image contents.

COPYRIGHT NOTICE/PERMISSION

A portion of the disclosure of this patent document contains materialwhich is subject to copyright protection. The copyright owner has noobjection to the facsimile reproduction by anyone of the patent documentor the patent disclosure as it appears in the Patent and TrademarkOffice patent file or records, but otherwise reserves all copyrightrights whatsoever. The following notice applies: Copyright © 2006, SonyElectronics Inc., All Rights Reserved.

BACKGROUND OF THE INVENTION

Modern televisions and monitors typically include multiple differentimage adjustment mechanisms. A user can navigate through a display menuto manually adjust image display settings such as brightness, contrast,sharpness, hue, etc. As image display settings are adjusted, an imagewill appear differently on the television or monitor.

Some televisions and monitors also include one or more preset displaymodes, each of which includes preset hue, sharpness, contrast,brightness, and so on. A user can select a preset display mode bynavigating through the display menu. Some display modes can moreaccurately display certain images than other display modes. For example,a sports display mode can include image display settings that aregenerally considered best for watching sporting events. Likewise, acinema display mode can include image display settings that aregenerally considered best for watching most movies. However, to takeadvantage of such preset display modes, a user must manually change thedisplay mode each time the user changes what he or she is watching.Moreover, the preset display modes apply throughout a movie (for everyscene), sporting event, etc. However, a particular preset display modecan not include the best image display settings throughout a program,movie or sporting event.

SUMMARY OF THE INVENTION

A video input signal is analyzed to detect image properties and imagecontent, wherein detecting the image content includes automaticallyderiving image features. A content group is determined for the videoinput signal based on the detected image content. Each content groupincludes predefined image properties. Image display settings areadjusted based on a difference between the detected image properties andthe predefined image properties.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1A illustrates one embodiment of a image adjustment device;

FIG. 1B illustrates an exemplary signal analyzer, in accordance with oneembodiment of the present invention;

FIG. 1C illustrates an exemplary group determiner, in accordance withone embodiment of the present invention

FIG. 2A illustrates an exemplary multimedia system, in accordance withone embodiment of the present invention;

FIG. 2B illustrates an exemplary multimedia system, in accordance withanother embodiment of the present invention;

FIG. 2C illustrates an exemplary multimedia system, in accordance withyet another embodiment of the present invention;

FIG. 3 illustrates a method of modifying an image, in accordance withone embodiment of the present invention;

FIG. 4 illustrates a method of modifying an image, in accordance withanother embodiment of the present invention;

FIG. 5 illustrates a method of modifying an image, in accordance withyet embodiment of the present invention;

FIG. 6 illustrates a method of modifying an input signal, in accordancewith one embodiment of the present invention;

FIG. 7 illustrates a block diagram of a machine in the exemplary form ofa computer system, on which embodiments of the present invention canoperate.

DETAILED DESCRIPTION OF THE INVENTION

In the following detailed description of embodiments of the invention,reference is made to the accompanying drawings in which like referencesindicate similar elements, and in which is shown by way of illustrationspecific embodiments in which the invention can be practiced. Theseembodiments are described in sufficient detail to enable those skilledin the art to practice the invention, and it is to be understood thatother embodiments can be utilized and that logical, mechanical,electrical, functional and other changes can be made without departingfrom the scope of the present invention. The following detaileddescription is, therefore, not to be taken in a limiting sense, and thescope of the present invention is defined only by the appended claims.

For the sake of clarity, the following definitions of terms areprovided.

The term “signal properties” refers to properties of a video signal orstill image signal. Signal properties can include metadata (e.g., of atitle, time, date, GPS coordinates, etc.), signal structure (e.g.,interlaced or progressive scan), encoding (e.g., MPEG-7), signal noise,etc.

The term “image properties” refers to properties of an image that can bedisplayed from a signal. Image properties can include image size, imageresolution, frame rate, dynamic range, color cast (predominance of aparticular color), histograms (number of pixels at each grayscale valuewithin an image for each color channel), blur, image noise, etc.

The term “image contents” refers to the objects and scenes, as well asproperties of the objects and scenes, that viewers will recognize whenthey view an image (e.g., the objects and scenes acquired by a camera).Image contents include high level image features (e.g., faces,buildings, people, animals, landscapes, etc.) and low level imagefeatures (e.g., textures, real object colors, shapes, etc.).

The term “image display settings” refers to settings of a display thatcan be adjusted to modify the way an image appears on the display. Imagedisplay settings are a property of a display device, where as signalproperties, image properties and image contents are properties of asignal. Image display settings can include screen resolution (as opposedto image resolution), refreshment rate, brightness, contrast, hue,saturation, white balance, sharpness, etc.

The term “displayed image properties” refers to the properties of animage after it has been processed by a display device (or imageadjustment device). Displayed image properties can include the sameparameters as “image properties,” but the values of the parameters maybe different. Image display settings typically affect displayed imageproperties.

Beginning with an overview of the operation of the invention, FIG. 1illustrates one embodiment of an image adjustment device 100. Imageadjustment device 100 can modify one or more image display settings(e.g., contrast, brightness, hue, etc.) to cause displayed imageproperties (e.g., dynamic range, blur, image noise, etc.) to differ fromimage properties of an input signal. Alternatively, image adjustmentdevice 100 can modify an input signal to generate an output signalhaving modified image properties. To modify the image display settings,image adjustment device 100 can analyze a video input to detect imageproperties and image content. Image adjustment device 100 can determinea content group for (and apply the content group to) the video inputsignal based on the detected image content, and apply an appropriatecontent group. The content group can include predefined image propertiesthat are compared to the detected image properties to determine how tomodify the image display settings. Modifying the image display settingscan generate an image that is easier to see, more aestheticallypleasing, a more accurate representation of a recorded image, etc.

Image adjustment device 100 can be used to intercept and modify a videosignal at any point between a signal source and an ultimate display ofthe video signal. For example, the image adjustment device 100 can beincluded in a television or monitor, a set top box, a server side orclient side router, etc. While the invention is not limited to anyparticular logics or algorithms for content detection, content groupassignment or image display setting adjustment, for sake of clarity asimplified image adjustment device 100 is described.

In one embodiment, image adjustment device 100 includes logic executedby a microcontroller 105, field programmable gate array (FPGA),application specific integrated circuit (ASIC), or other dedicatedprocessing unit. In another embodiment, image adjustment device 100 caninclude logic executed by a central processing unit. Alternatively,image adjustment device 100 can be implemented as a series of statemachines (e.g., an internal logic that knows how to perform a sequenceof operations), logic circuits (e.g., a logic that goes through asequence of events in time, or a logic whose output changes immediatelyupon a changed input), or a combination of a state machines and logiccircuits.

In one embodiment, image adjustment device 100 includes an inputterminal 105, signal analyzer 110, frame rate adjustor 112, groupdeterminer 115, image adjustor 120, audio adjustor 125 and outputterminal 130. Alternatively, one or more of the frame rate adjustor 112,group determiner 115, image adjustor 120 and audio adjustor 125 can beomitted.

Input terminal 105 receives an input signal. The input signal can be avideo input signal or a still image input signal. Examples of videoinput signals that can be received include digital video signals (e.g.,an advanced television standards committee (ATSC) signal, digital videobroadcasting (DVB) signal, etc.) or analog video signals (e.g., anational television standards committee (NTSC) signal, phase alternatingline (PAL) signal, sequential color with memory (SECAM) signal,PAL/SECAM signal, etc.). Received video and/or still image signals canbe compressed (e.g., using motion picture experts group 1 (MPEG-1),MPEG-2, MPEG-4, MPEG-7, video codec 1 (VC-1), etc.) or uncompressed.Received video input signals can be interlaced or non-interlaced.

Input terminal 105 is coupled with signal analyzer 110, to which it canpass the received input signal. Signal analyzer 110 can analyze theinput signal to determine signal properties, image contents and/or imageproperties. Examples of image properties that can be determined includeimage size, image resolution, dynamic range, color cast, etc. Examplesof signal properties that can be determined include encoding, signalnoise, signal structure (interlaced or progressive scan), etc. In oneembodiment, determining image contents includes deriving image featuresof the input signal. Both high level image features (e.g., faces,buildings, people, animals, landscapes, etc.) and low level imagefeatures (e.g., texture, color, shape, etc.) can be derived. Signalanalyzer 110 can derive both low level and high level image featuresautomatically. Signal analyzer 110 can then determine content based on acombination of derived image features. Signal Analyzer 110 is discussedin more detail below with reference to FIG. 1B.

Referring to FIG. 1A, in one embodiment, image adjustment device 100includes frame rate adjustor 112. In one embodiment, frame rate adjustor112 has an input connected to an output of signal analyzer 110 (asshown). In another embodiment, frame rate adjustor 112 has an inputconnected to an output of group determiner 115, image adjustor 120and/or audio adjustor 125.

Frame rate adjustor 112 can adjust a frame rate of a video input signalbased on signal properties, image contents and/or motion detected bysignal analyzer 110. In one embodiment, frame rate adjustor 112increases the frame rate if image features are detected to have a motionthat is greater than a threshold value. In one embodiment, a thresholdof the frame rate is determined based on a speed of the moving objects.For example, a threshold may indicate that between frames an objectshould not move more than three pixels. Therefore, if an object isdetected to move more than three pixels between two frames, the framerate can be increased until the object moves three pixels or lessbetween frames.

The amount of increase in the frame rate can also depend on a differencebetween the threshold and the detected motion. Alternatively, a lookuptable or other data structure can include multiple entries, each ofwhich associates motion vectors or amount of blur with specified framerates. In one embodiment, an average global movement is used todetermine whether to adjust the frame rate. Alternatively, frame rateadjustment can be based on a fastest moving image feature.

In one embodiment, group determiner 115 has an input connected with anoutput of frame rate adjustor 112. In an alternative embodiment, groupdeterminer 115 has an input connected with an output of signal analyzer110. Group determiner 115 receives low level and high level featuresthat have been derived by signal analyzer 110. Based on the low leveland/or high level features, group determiner 115 can determine a contentgroup (also referred to as a content mode) for the input signal, andassign the determined content group to the signal. For example, if thederived image features include a sky, a natural landmark and no faces, alandscape group can be determined and assigned. If, on the other hand,multiple faces are detected without motion, a portrait group can bedetermined and assigned.

In one embodiment, group determiner 115 assigns a single content groupto the image. Alternatively, group determiner 115 can divide the imageinto multiple image portions. The image can be divided into multipleimage portions based on spatial arrangements (e.g., top half of imagevs. lower half of image), based on boundaries of derived features, orbased on other criteria. Group determiner 115 can then determine a firstcontent group for a first image portion containing first image features,and determine a second content group for a second image portioncontaining second image features. The input signal can also be dividedinto more than two image portions, each of which can be assigneddifferent content groups. Group determiner 115 is discussed in moredetail below with reference to FIG. 1C.

Referring to FIG. 1A, in one embodiment, the image adjustment device 100includes an image adjustor 120 having an input coupled with an output ofgroup determiner 115. As described above, each content group includes acollection of predefined image properties. Image adjustor 120 can adjustone or more image display settings based on a comparison of detectedimage properties (as detected by signal analyzer 110) to predefinedimage properties of the assigned content group or groups. A differencebetween the detected image properties and the predefined imageproperties can determine a magnitude of the adjustments to the imagedisplay settings. Alternatively, image adjustor 120 can modify the inputsignal such that when displayed on a display having known image displaysettings, displayed images will have the predefined image properties.

In one embodiment, the image adjustment device 100 includes an audioadjustor 125 having an input coupled with an output of group determiner115. Audio adjustor 125 can adjust one or more audio settings (e.g.,volume, equalization, audio playback speed, etc.) based on a comparisonof detected audio properties (as detected by signal analyzer 110) topredefined audio properties of the assigned content group or groups.Where multiple content groups have been assigned to an input signal,audio settings can be modified based on differences between the detectedaudio properties and the predefined audio properties of one of thecontent groups, or based on an average or other combination of the audioproperties of assigned content groups.

Output terminal 130 can have an input connected with outputs of one ormore of frame rate adjustor 112, image adjustor 120 and audio adjustor125. In one embodiment, output terminal 130 includes a display thatdisplays the input signal using adjusted image display settings and/oran adjusted frame rate. Output terminal 130 can also include speakersthat emit an audio portion of the input signal using adjusted audiosettings.

In another embodiment, output terminal 130 transmits an output signal toa device having a display and/or speakers. Output terminal 130 canproduce the output signal based on adjustment directives received fromone or more of the frame rate adjustor 112, image adjustor 120 and audioadjustor 125. The output signal, when displayed on a display devicehaving known image display settings, can have the predefined imageproperties of the determined content group (or content groups). Theoutput signal, when displayed on the display device, can also have anadjusted frame rate and/or predefined audio properties of the determinedcontent group.

In yet another embodiment, output terminal 130 transmits the inputsignal to a device having a display and/or speakers along with an imagedisplay settings modification signal. The image display settingsmodification signal can direct a display device to adjust its imagedisplay settings to display the image such that predefined imageproperties of determined content groups are shown. The image displaysettings modification signal can be metadata that is appended to theinput signal.

FIG. 1B illustrates an exemplary signal analyzer 110, in accordance withone embodiment of the present invention. Signal analyzer 110 can analyzethe input signal to determine signal properties, image contents and/orimage properties. In one embodiment, determining image contents includesderiving image features of the input signal. Both high level imagefeatures (e.g., faces, buildings, people, animals, landscapes, etc.) andlow level image features (e.g., texture, color, shape, etc.) can bederived. Signal analyzer 110 can then determine content based on acombination of derived image features.

If a received signal is compressed, one or more signal properties, imageproperties and/or image content can be determined while the signalremains compressed. The signal can then be decompressed, and additionalsignal properties, image properties and/or image contents cansubsequently be determined. Some signal properties, image propertiesand/or image contents can be easier to detect while the image iscompressed, while other signal properties, image properties and/orcontents can be easier to determine after the image is decompressed. Forexample, noise detection can be performed on compressed data, while facedetection can be performed on decompressed data.

Signal analyzer 110 can analyze the signal dynamically as the signal isreceived on a frame-by-frame basis (e.g., every frame, every otherframe, etc., can be analyzed separately), or as a sequence of frames(e.g., every three frames can be analyzed together). Where a sequence offrames is analyzed together, image contents appearing in one of theframes can be applied to each of the frames in the sequence.Alternatively, image contents may not be applied to the frames unlessthe image content appears in each frame in the sequence.

In one embodiment, signal analyzer 110 includes a signal propertiesdetector 135, an image properties detector 140. In further embodiments,signal analyzer 110 also includes a motion detector 150 and an audioproperties detector 155.

Signal properties detector 135 analyzes the input signal to determinesignal properties (e.g., encoding, signal noise, signal structure,etc.). For example, signal properties detector 135 may analyze the inputsignal to read any metadata attached to the signal. Such metadata canprovide information about the image contents and/or image properties.For example, MPEG-7 encoded video signals include metadata that candescribe image contents, audio information, and other signal details.Signal properties detector 135 may also determine, for example, a signalto noise ratio, or whether the signal is interlaced or non-interlaced.

In one embodiment, image properties detector 140 determines imageproperties such as image size, image resolution, color cast, etc., ofthe input signal. In one embodiment, image properties detector 140derives image features of the input signal, which may then be used todetermine image contents of the input signal. Image properties detector140 may derive both high level image features (e.g., faces, buildings,people, animals, landscapes, etc.) and low level image features (e.g.,texture, color, shape, etc.) from the input signal. Image propertiesdetector 140 can derive both low level and high level image featuresautomatically. Exemplary techniques for deriving some possible imagefeatures known to those of ordinary skill in the art of content basedimage retrieval are described below. However, other known techniques canbe used to derive the same or different image features.

In one embodiment, image properties detector 140 analyzes the inputsignal to derive the colors contained in the signal. Derived colors andthe distribution of the derived colors within the image can then be usedalone, or with other low level features, to determine high level imagefeatures and/or image contents. For example, if the image has a largepercentage of blue in a first image portion and a large percentage ofyellow in another image portion, the image can be an image of abeachscape. Colors can be derived by mapping color histograms. Suchcolor histograms can then be compared against stored color histogramsassociated with specified high level features. Based on the comparison,information of the image contents can be determined. Colors can also bedetermined and compared using different techniques (e.g., by usingGaussian models, Bayes classifiers, etc.).

In one embodiment, image properties detector 140 uses elliptical colormodels to determine high level features and/or image contents. Anelliptical color model may be generated for a high level feature bymapping pixels of a set of images having the high level feature on theHue, Saturation, Value (HSV) color space. The set of images can then betrained using a first training set of images having the high levelfeature, and a second training set of images that lacks the high levelfeature. An optimal elliptical color model is then determined bymaximizing a statistical distance between the first training set ofimages and the second training set of images.

An image (e.g., of the input signal) can then be mapped on the HSV colorspace, and compared to the elliptical color model for the high levelfeature. The percentage of pixels in the image that match pixels in theelliptical color model can be used to estimate a probability that theimage includes the high level image feature. This comparison can bedetermined using the equation:d(I,T)={circumflex over (t)}−îwhere {circumflex over (t)} is the amount of pixels in the colorelliptical model and î is the amount of pixels of the image that arealso in the color elliptical model. If the image has a large amount ofpixels î that match those of the color elliptical model {circumflex over(t)}, then a distance d(I,T) tends to be small, and it can be determinedthat the high level feature is present in the image.

If a high level feature includes multiple different colors, such as redflowers and green leaves, the high level feature can be segmented. Aseparate elliptical color model can be generated for each segment. Animage mapped on the HSV color space can then be compared to each colorelliptical model. If distances d(I,T) are small for both colorelliptical models, then it can be determined that the high level featureis present in the image.

In one embodiment, image properties detector 140 analyzes the inputsignal to derive textures contained in the signal. Texture can bedetermined by finding visual patterns in images contained in the inputsignal. Texture can also include information on how images and visualpatterns are spatially defined. Textures can be represented as textureelements (texels), which can be placed into a number of sets (e.g.,arrays) depending on how many textures are detected in the image, howthe texels are arranged, etc. The sets can define both textures andwhere in the image the textures are located. Such texels and sets can becompared to stored texels and sets associated with specified high levelfeatures to determine information of the image contents.

In one embodiment, image properties detector 140 uses wavelet-basedtexture feature extraction to determine high level features and/or imagecontents. Image properties detector 140 can include multiple texturemodels, each for specific image features or image contents. Each texturemodel can include wavelet coefficients and various values associatedwith wavelet coefficients, as described below. The wavelet coefficientsand associated values can be compared to wavelet coefficients andassociated values extracted from an input signal to determine whetherany of the high level features and/or image contents are included in thevideo input signal.

Wavelet coefficients can be calculated, for example, by performing aHaar wavelet transformation on an image. Initial low level coefficientscan be calculated by combining adjacent pixel values according to theformula:

$L_{i} = {\left( {P_{2i} + P_{{{2i} + 1}\;}} \right)\frac{1}{\sqrt{2}}}$where L is a low-frequency coefficient, i is an index number of thewavelet coefficient, and P is a pixel value from the image. Initial highlevel coefficients can be calculated by combining adjacent pixel valuesaccording to the formula:

$H_{i} = {\left( {P_{2i} - P_{{{2i} + 1}\;}} \right)\frac{1}{\sqrt{2}}}$where H is a high-frequency coefficient, i is an index number of thewavelet coefficient, and P is a pixel value from the image.

The Haar wavelet transformation can be applied recursively, such that itis performed on each initial low level coefficient and high levelcoefficient to produce second level coefficients. It can then be appliedto the second level coefficients to produce third level coefficients,and so on, as necessary. In one embodiment, four levels of coefficientsare used (e.g., four recursive wavelet transformations are calculated).

Wavelet coefficients from the multiple levels can be used to calculatecoefficient mean absolute values for various subbands of the multiplelevels. For example, coefficient mean absolute values μ for a givensubband (e.g., the low-high (LH) subband) can be calculated using theformula:

$\mu_{{LH}{(i)}} = {\frac{1}{MN}{\sum\limits_{m = 1}^{M}{\sum\limits_{n = 1}^{N}{{W_{{LH}{(i)}}\left\lbrack {m,n} \right\rbrack}}}}}$where LH(i) is an LH subband at level i, W is a wavelet coefficient, mis a coefficient row, n is a coefficient column, M is equal to a totalnumber of coefficient rows, and N is equal to a total number ofcoefficient columns. Coefficient mean absolute values can also bedetermined for the other subbands (e.g., high-high (HH), high-low (HL)and low-low (LL)) of the multiple levels.

Wavelet coefficients for the multiple levels can be used to calculatecoefficient variance values for the various subbands of the multiplelevels. For example, coefficient variance values σ² can be calculatedfor a given subband LH using the formula:

$\sigma_{{LH}{(i)}}^{2} = {\frac{1}{MN}{\sum\limits_{m = 1}^{M}{\sum\limits_{n = 1}^{N}\left( {{{W_{{LH}{(i)}}\left\lbrack {m,n} \right\rbrack}} - \mu_{{LH}{(i)}}} \right)^{2}}}}$where LH(i) is an LH subband at level i, W is a wavelet coefficient, mis a coefficient row, n is a coefficient column, M is equal to a totalnumber of coefficient rows, N is equal to a total number of coefficientcolumns, and μ is a corresponding coefficient mean absolute value.Coefficient variance values can also be determined for the othersubbands (e.g., HH, HL and LL) of the multiple levels.

Mean absolute texture angles can then be calculated using thecoefficient mean absolute values according to the formula:

$\theta_{\mu_{(i)}} = {\arctan\;\frac{\mu_{{LH}{(i)}}}{\mu_{{HL}{(i)}}}}$where θ_(μ(i)) is a mean absolute value texture angle, μ is acoefficient mean absolute value, i is a subband level, LH is a Low-Highsubband, and HL is a High-Low subband.

Variance value texture angles can be calculated using the coefficientvariance values according to the formula:

$\theta_{\sigma_{(i)}} = {\arctan\;\frac{\sigma_{{LH}{(i)}}}{\sigma_{{HL}{(i)}}}}$where θ_(σ(i)) is the variance value texture angle, σ is a coefficientvariance value, i is a subband level, LH is a Low-High subband, and HLis a High-Low subband. The mean absolute texture angles and variancevalue texture angles together indicate how a texture is oriented in animage.

Total mean absolute values can be calculated according to the formula:μ_((i))=[μ_(LH(i)) ²+μ_(HH(i)) ²+μ_(HL(i)) ²]

where μ_((i)) is a total mean absolute value, i is a wavelet level,μ_(LH(i)) is a coefficient mean absolute value for an LH subband,μ_(HH(i)) is a coefficient mean absolute value for an HH subband, andμ_(HL(i)) is a coefficient mean absolute value for an HL subband.

Total variance values can be calculated according to the formula:σ_((i)) ²=σ_(LH(i)) ²+σ_(HH(i)) ²+σ_(HL(i)) ²where σ_((i)) is a total variance value, i is a wavelet level, σ_(LH(i))is a coefficient variance value for an LH subband, σ_(HH(i)) is acoefficient variance value for an HH subband, and σ_(HL(i)) is acoefficient variance value for an HL subband.

The total mean absolute values and total variance values can be used tocalculate distance values between a texture model and an image of aninput signal. The above discussed values can be calculated for areference image or images having a high level feature to generate atexture model. The values can then be calculated for an input signal.The values for the video input signal are compared to the values for thetexture model. If a distance D (representing texture similarity) betweenthe two is small, then the input signal can be determined to include thehigh level feature. The distance D can be calculated using the equation:

$D = {{\sum\limits_{i = 1}^{4}{{\frac{1}{2^{i}}\left\lbrack {{\mu_{(i)}^{T}{{\theta_{\mu_{(i)}}^{T} - \theta_{\mu_{(i)}}^{I}}}} + \frac{1}{5}} \right.}\theta_{\sigma_{(i)}}^{T}}} - \theta_{\sigma_{(i)}}^{I}}$where D is a distance value, T indicates the texture model, I indicatesthe test image, i is a wavelet level, μ is a total mean absolute value,σ is a total variance value, θ_(σ) is a variance value texture angle,and θ_(μ) is a mean absolute value texture angle.

In one embodiment, image properties detector 140 determines shapes ofregions within the image (e.g., image portions). Shapes can bedetermined by applying segmentation, blob extraction, edge detectionand/or other known shape detection techniques to the image. Determinedshapes can then be compared to a database of stored shapes to determineinformation about the image contents. Image properties detector 140 canthen determine image content based on the detected image propertiesand/or image features. In one embodiment, image properties detector 140uses a combination of derived image features to determine imagecontents.

In one embodiment, image properties detector 145 uses low level derivedimage features to determine high level image features. For example, thederived low level image features can be used to determine a person orpersons, a landscape, a building, etc. Image properties detector 145 cancombine the low level image features described above, as well as otherlow level image features known in the art, to determine high level imagefeatures. For example, a first shape, texture and color can correspondto a face, while a second shape, texture and color can correspond to atree.

In one embodiment, signal analyzer 110 includes a motion detector 150.Motion detector 150 detects motion within images of a video inputsignal. Motion detector 150 can detect moving image portions, forexample, by using motion vectors. Such motion vectors can be used todetermine global motion (amount of total motion within a scene), andlocal motion (amount of motion within one or more image portions and/oramount that one or more features is detected to move). Motion detector150 can also detect moving image portions by analyzing image propertiessuch as blur. Directional blur can be detected that indicates movementof an object (or a camera) in the direction of the blur. A degree ofblur can be used to estimate a speed of the moving object.

In one embodiment, signal analyzer 110 includes an audio propertiesdetector 155. Audio properties detector 155 detects audio properties(features) of the input signal. Audio properties often exhibit a strongcorrelation to image features. Accordingly, in one embodiment such audioproperties are used to better determine image contents. Examples ofaudio properties that can be detected and used to determine imagefeatures include cepstral flux, multi-channel cochlear decomposition,cepstral flux, multi-channel vectors, low energy fraction, spectralflux, spectral roll off point, zero crossing rate, variance of zerocrossing rate, energy, and variance of energy.

FIG. 1C illustrates an exemplary group determiner 115, in accordancewith one embodiment of the present invention. Group determiner 115includes multiple content groups 160. Group determiner 115 receives lowlevel and high level features that have been derived by signal analyzer110. Based on the low level and/or high level features, group determiner115 can determine a content group (also referred to as a content mode)for the input signal from the multiple content groups 160, and assignthe determined content group to the signal. For example, if the derivedimage features include a sky, a natural landmark and no faces, alandscape group can be determined and assigned. If, on the other hand,multiple faces are detected without motion, a portrait group can bedetermined and assigned. In one embodiment, determining a content groupincludes automatically assigning that content group to the signal.

In one embodiment, group determiner 115 assigns a single content groupfrom the multiple content groups 160 to the image. Alternatively, groupdeterminer 115 can divide the image into multiple image portions. Theimage can be divided into multiple image portions based on spatialarrangements (e.g., top half of image vs. lower half of image), based onboundaries of derived features, or based on other criteria. Groupdeterminer 115 can then determine a first content group for a firstimage portion containing first image features, and determine a secondcontent group for a second image portion containing second imagefeatures. The input signal can also be divided into more than two imageportions, each of which can be assigned different content groups.

Each of the content groups 160 can include predefined image propertiesfor the reproduction of images in a specified environment (e.g., acinema environment, a landscape environment, etc.). Each of the contentgroups can also include predefined audio properties for audioreproduction. Content groups 60 preferably provide optimal imageproperties and/or audio properties for the specified environment. Thepredefined image properties can be achieved by modifying image displaysettings of a display device. Alternatively, the predefined imageproperties can be achieved by modifying the input signal such that whendisplayed by a display having known image display settings, the imagewill be shown with the predefined image properties.

Exemplary content groups 160 are described below. It should beunderstood that these content groups 160 are for purposes ofillustration, and that additional content groups 160 are envisioned.

In one embodiment, the content groups 160 include a default contentgroup. The default content group can automatically be applied to allimages until a different content group is determined. The defaultcontent group can include predefined image properties that provide apleasing image in the most sets of circumstances. In one embodiment, thedefault content group provides increased sharpness, high contrast andhigh brightness for optimally displaying television programs.

In one embodiment, the content groups 160 include a portrait contentgroup. The portrait content group includes predefined image propertiesthat provide for images with optimal skin reproduction. The portraitcontent group can also include predefined image properties that betterdisplay still images. In one embodiment, the portrait content groupprovides predefined color settings for magenta, red and yellow tonesthat enable production of a healthy skin color with minimum color bias,and lowers sharpness to smooth out skin texture.

In one embodiment, the content groups 160 include a landscape contentgroup. The landscape content group can include predefined imageproperties that enable vivid reproduction of images in the green-to-bluerange (e.g., to better display blue skies, blue oceans, and greenfoliage). Landscape content group can also include a predefined imageproperty of an increased sharpness.

In one embodiment, the content groups 160 include a sports contentgroup. The sports content group can provide accurate depth andlight/dark gradations, sharpen images by using gamma curve andsaturation adjustments, and adjust color settings to that greens (e.g.,football fields) appear more vibrant.

In one embodiment, the content groups 160 include a cinema contentgroup. The cinema content group can use gamma curve correction toprovide color compensation to clearly display dim scenes, whilesmoothing out image motion.

Examples of other possible content groups 160 include a text contentgroup (in which text should have a high contrast with a background andhave sharp edges), a news broadcast content group, a still picturecontent group, a game content group, a concert content group, and a homevideo content group.

FIG. 2A illustrates an exemplary multimedia system 200, in accordancewith one embodiment of the present invention. Multimedia system 200includes a signal source 205 coupled with a display device 240. Signalsource 205 can generate one or more of a video signal, a still imagesignal and an audio signal. In one embodiment, signal source is abroadcast station, cable or satellite television provider, or otherremote multimedia service provider. Alternatively, signal source can bea video cassette recorder (VCR), digital video disk (DVD) player, highdefinition digital versatile disk (HD-DVD) player, Blu-Ray® player, orother media playback device.

The means by which signal source 205 couples with display device 240 candepend on properties of the signal source 205 and/or properties ofdisplay device 240. For example, if signal source 205 is a broadcasttelevision station, signal source 205 can be coupled with display device240 via radio waves carrying a video signal. If on the other hand signalsource 205 is a DVD player, for example, signal source 205 can couplewith display device via an RCA cable, DVI cable, HDMI cable, etc.

Display device 240 can be a television, monitor, projector, or otherdevice capable of displaying still and/or video images. Display device240 can include an image adjustment device 210 coupled with a display215. Image adjustment device 210 in one embodiment corresponds to imageadjustment device 100 of FIG. 1A.

Display device 240 receives an input signal 220 from signal source 205.Image adjustment device 210 can modify image display settings such thatan output signal shown on display 215 has predefined image properties ofone or more determined content groups.

FIG. 2B illustrates an exemplary multimedia system 250, in accordancewith another embodiment of the present invention. Multimedia system 250includes a media player 255 coupled with a display device 260. Mediaplayer 255 can be a VCR, DVD player, HD-DVD player, Blu-Ray® player,digital video recorder (DVR), or other media playback device, and cangenerate one or more of a video signal, a still image signal and anaudio signal. In one embodiment, media player 255 includes imageadjustment device 210, which can correspond to image adjustment device100 of FIG. 1A.

Image adjustment device 210 can modify an input signal produced duringmedia playback to generate an output signal 225. Output signal 225 canbe transmitted to display device 260, which can display the outputsignal 225. In one embodiment, the signal adjustment device 210 adjuststhe input signal such that the output signal, when displayed on displaydevice 260, will show images having predefined image propertiescorresponding to one or more image content groups. To determine how tomodify the input signal to display the predefined image properties ondisplay device 260, signal adjustment device 210 can receive displaysettings 227 from display device 260. Given a specified set of displaysettings 227, the output signal 225 can be generated such that thepredefined image properties will be displayed.

In another embodiment, output signal includes an unchanged input signal,as well as an additional signal (or metadata attached to the inputsignal) that directs the display device to use specified image displaysettings.

FIG. 2C illustrates an exemplary multimedia system 270, in accordancewith yet another embodiment of the present invention. Multimedia system270 includes a signal source 205, an image adjustment device 275 and adisplay device 260.

As illustrated, image adjustment device 275 is disposed between, andcoupled with, the signal source 205 and display device 260. Imageadjustment device 275 can correspond to image adjustment device 100 ofFIG. 1A. Image adjustment device 275 in one embodiment is a stand alonedevice used to modify signals transmitted to display device 260.Alternatively, image adjustment device 275 can be a component in, forexample, a set top box, digital video recorder (DVR), cable box,satellite receiver, and so on.

Image adjustment device 275 can receive an input signal 220 generated bysignal source 205. Image adjustment device 275 can then modify the inputsignal 220 to generate output signal 225, which can then be transmittedto display device 260. Display device 260 can then display output signal225. Prior to generating output signal 225, signal adjustment device 275can receive display settings 227 from display device 260. The receiveddisplay settings can then be used to determine how the input signalshould be modified to generate the output signal.

The particular methods of the invention are described in terms ofcomputer software with reference to a series of flow diagrams,illustrated in FIGS. 3-6. Flow diagrams illustrated in FIGS. 3-6illustrate methods of modifying an image, and can be performed by imageadjustment device 100 of FIG. 1A. The methods constitute computerprograms made up of computer-executable instructions illustrated, forexample, as blocks (acts) 305 until 335 in FIG. 3. Describing themethods by reference to a flow diagram enables one skilled in the art todevelop such programs including such instructions to carry out themethods on suitably configured computers or computing devices (theprocessor of the computer executing the instructions fromcomputer-readable media, including memory). The computer-executableinstructions can be written in a computer programming language or can beembodied in firmware logic. If written in a programming languageconforming to a recognized standard, such instructions can be executedon a variety of hardware platforms and for interface to a variety ofoperating systems.

The present invention is not described with reference to any particularprogramming language. It will be appreciated that a variety ofprogramming languages can be used to implement the teachings of theinvention as described herein. Furthermore, it is common in the art tospeak of software, in one form or another (e.g., program, procedure,process, application, module, logic, etc.), as taking an action orcausing a result. Such expressions are merely a shorthand way of sayingthat execution of the software by a computer causes the processor of thecomputer to perform an action or produce a result. It will beappreciated that more or fewer processes can be incorporated into themethods illustrated in FIGS. 3-6 without departing from the scope of theinvention, and that no particular order is implied by the arrangement ofblocks shown and described herein.

Referring first to FIG. 3, the acts to be performed by a computerexecuting a method 300 of modifying an image are shown.

In a particular implementation of the invention, method 300 receives(e.g., by input terminal 105 of FIG. 1A) a video input signal (block305). At block 310, the video input signal is analyzed (e.g., by signalanalyzer 110 of FIG. 1A) to detect image properties and image contents.In further embodiments, the video input signal can also be analyzed todetect signal properties (e.g., to determine whether the signal isinterlaced or non-interlaced, whether the signal is compressed oruncompressed, a noise level of the signal, etc). If the signal iscompressed, it can be necessary to decompress the signal beforedetermining one or more image properties and/or image contents.

The video input signal can be analyzed on a frame-by-frame basis.Alternatively, a sequence of frames of the video input signal can beanalyzed together such that any modifications occur to each of theframes in the sequence. Image properties can include image size, imageresolution, dynamic range, color cast, blur, etc. Image contents can bedetected by deriving low level image features (e.g., texture, color,shape, etc.) and high level image features (e.g., faces, people,buildings, sky, etc.). Such image features can be derived automaticallyupon receipt of the video input signal.

At block 315, a content group is determined (e.g., by group determiner115 of FIG. 1A) for the video input signal. The content group can bedetermined based on the detected image contents. In a furtherembodiment, the content group is determined using metadata included inthe video signal. Metadata can provide information about the imagecontent and/or image properties. For example, the metadata can identifya current scene of a movie as an action scene. At block 318, the contentgroup is applied to the input video signal.

At block 320, it is determined whether detected image properties matchpredefined image properties associated with the determined contentgroup. If the detected image properties match the predefined imageproperties, the method proceeds to block 330. If the detected imageproperties do not match the predefined image properties, the methodcontinues to block 325.

At block 325, image display settings and/or the video input signal areadjusted (e.g., by image adjustor 120 of FIG. 1A). The image displaysettings and/or video input signal can be adjusted based on a differencebetween the detected image properties and the predefined imageproperties. This permits the input signal to be displayed such that thepredefined image properties are shown. At block 330, the video inputsignal is displayed. The method then ends.

Though method 300 is shown to have specific blocks in which differenttasks are performed, it is foreseeable that some of the tasks performedby some of the blocks can be separated and/or combined. For example,block 310 shows that the video input signal is analyzed to determine theimage contents and image properties together. However, the video signalcan be analyzed to detect image contents without determining imageproperties. A content group can then be determined, after which thesignal can be analyzed to determine image properties. In anotherexample, block 315 (determining a content group) and block 318(assigning a content group) can be combined into a single block (e.g.,into a single action). Other modifications to the described method arealso foreseeable.

Referring now to FIG. 4, the acts to be performed by a computerexecuting a method 400 of modifying an image are shown.

In a particular implementation of the invention, method 400 includesreceiving (e.g., by input terminal 105 of FIG. 1A) a video input signal(block 405). At block 410, image properties, signal properties and/orimage contents are detected (e.g., by signal analyzer 110 of FIG. 1A).At block 412, the image is divided into a first image portion and asecond image portion. In one embodiment, the image is divided into thefirst image portion and the second image portion based on boundaries ofimage features. Alternatively, the division can be based on separationof the image into equal or unequal parts. For example, the image can bedivided into a first half (e.g., top half or right half) and a secondhalf (e.g., bottom half or left half). The first image portion caninclude first image features and the second image portion can includesecond image features.

At block 415, a first content group is determined (e.g., by groupdeterminer 115 of FIG. 1A) for the first image portion and a secondcontent group is determined for the second image portion. Determiningthe first content group and the second content group can includeapplying a first content group, and a second content group,respectively. The first content group can include first predefined imageproperties, and the second content group can include second predefinedimage properties. The first content group and second content group canbe determined based on the detected image contents of each imageportion, respectively.

At block 420, it is determined whether detected image properties matchpredefined image properties associated with the determined contentgroups and/or assigned content groups. If the detected image propertiesmatch the predefined image properties, the method proceeds to block 430.If the detected image properties do not match the predefined imageproperties, the method continues to block 425.

At block 425, image display settings and/or the video input signal areadjusted (e.g., by image adjustor 120 of FIG. 1A) such that the detectedimage properties match the predefined image properties of the firstimage portion and second image portion, respectively. Image displaysettings and/or the video input signal can be adjusted for the firstimage portion based on a difference between the first detected imageproperties and the first predefined image properties of first contentgroup. Image display settings and or the video input signal can beadjusted for the second image portion based on a difference between thesecond detected image properties and the second predefined imageproperties of the second content group. At block 430, the video inputsignal is displayed. The method then ends.

Referring now to FIG. 5, the acts to be performed by a computerexecuting a method 500 of modifying an audio output are shown.

In a particular implementation of the invention, method 500 includesreceiving (e.g., by input terminal 105 of FIG. 1A) a video input signal(block 505). At block 510, the video input signal is analyzed (e.g., bysignal analyzer 110 of FIG. 1A) to detect audio properties and imagecontents. Audio properties can include cepstral flux, multi-channelcochlear decomposition, multi-channel vectors, low energy fraction,spectral flux, spectral roll off point, zero crossing rate, variance ofzero crossing rate, energy, variance of energy, etc. At block 515, acontent group is determined (e.g., by group determiner 115 of FIG. 1A)for the video input signal. The content group can be determined based onthe detected image contents, and can include predefined audioproperties.

At block 520, it is determined whether detected audio properties matchpredefined audio properties associated with the determined contentgroup. If the detected audio properties match the predefined audioproperties, the method proceeds to block 530. If the detected audioproperties do not match the predefined audio properties, the methodcontinues to block 525.

At block 525, audio settings and/or an audio portion of the video inputsignal are adjusted (e.g., by audio adjustor 125 of FIG. 1A). The audiosettings and/or the audio portion of the video input signal can beadjusted based on a difference between the detected audio properties andthe predefined audio properties. Examples of audio settings that can beadjusted include volume, playback speed and equalization. At block 530,the video input signal is output (e.g., transmitted and/or played). Themethod then ends.

Referring now to FIG. 6, the acts to be performed by a computerexecuting a method 600 of modifying an image are shown.

In a particular implementation of the invention, method 600 includesreceiving (e.g., by input terminal 105 of FIG. 1A) a video input signal(block 606). At block 610, the video input signal is analyzed (e.g., bysignal analyzer 110 of FIG. 1A) to detect image contents. At block 615,it is determined whether the image contents are moving. Such adetermination can be made, for example, using motion vectors or bydetected a degree and direction of blur. At block 620, it is determinedhow fast the image contents are moving.

At block 630, a frame rate of the video signal is adjusted (e.g., byframe rate adjustor 112 of FIG. 1A) based on how fast the image contentsare moving. For example, the frame rate can be increased as the imagecontents are detected to move faster. At block 636, a sharpness of themoving objects is adjusted (e.g., by image adjustor 120 of FIG. 1A).Alternatively, a sharpness of the entire signal can be adjusted. Thesharpness adjustment can be based on how fast the image contents aremoving.

At block 640, the video input signal is output (e.g., transmitted and/ordisplayed) using the adjusted frame rate and the adjusted sharpness. Themethod then ends.

FIG. 7 illustrates a block diagram of a machine in the exemplary form ofa computer system 700 within which a set of instructions, for causingthe machine to perform any one or more of the methodologies discussedherein, can be executed. The exemplary computer system 700 includes aprocessing device (processor) 705, a memory 710 (e.g., read-only memory(ROM), a storage device, a static memory, etc.), and an input/output715, which communicate with each other via a bus 720. Embodiments of thepresent invention can be performed by the computer system 700, and/or byadditional hardware components (not shown), or can be embodied inmachine-executable instructions, which can be used to cause processor705, when programmed with the instructions, to perform the methodsdescribed above. Alternatively, the methods can be performed by acombination of hardware and software.

Processor 705 represents one or more general-purpose processing devicessuch as a microprocessor, central processing unit, or the like. Moreparticularly, the processor 705 can be a complex instruction setcomputing (CISC) microprocessor, reduced instruction set computing(RISC) microprocessor, very long instruction word (VLIW) microprocessor,or a processor implementing other instruction sets or processorsimplementing a combination of instruction sets. The processor 705 canalso be one or more special-purpose processing devices such as anapplication specific integrated circuit (ASIC), a field programmablegate array (FPGA), a digital signal processor (DSP), network processor,or the like.

The present invention can be provided as a computer program product, orsoftware, that can be stored in memory 710. Memory 710 can include amachine-readable medium having stored thereon instructions, which can beused to program exemplary computer system 700 (or other electronicdevices) to perform a process according to the present invention. Othermachine-readable mediums which can have instruction stored thereon toprogram exemplary computer system 700 (or other electronic devices)include, but are not limited to, floppy diskettes, optical disks,CD-ROMs, and magneto-optical disks, ROMs, RAMs, EPROMs, EEPROMs,magnetic or optical cards, flash memory, or other type of media ormachine-readable mediums suitable for storing electronic instructions.

Input/output 715 can provide communication with additional devicesand/or components. Thereby, input/output 715 can transmit data to andreceive data from, for example, networked computers, servers, mobiledevices, etc.

The description of FIG. 7 is intended to provide an overview of computerhardware and other operating components suitable for implementing theinvention, but is not intended to limit the applicable environments. Itwill be appreciated that the computer system 700 of FIG. 7 is oneexample of many possible computer systems which have differentarchitectures. One of skill in the art will appreciate that theinvention can be practiced with other computer system configurations,including multiprocessor systems, minicomputers, mainframe computers,and the like. The invention can also be practiced in distributedcomputing environments where tasks are performed by remote processingdevices that are linked through a communications network.

A device and method for modifying a signal has been described. Althoughspecific embodiments have been illustrated and described herein, it willbe appreciated by those of ordinary skill in the art that anyarrangement which is calculated to achieve the same purpose can besubstituted for the specific embodiments shown. This application isintended to cover any adaptations or variations of the presentinvention. For example, those of ordinary skill within the art willappreciate that image content detection algorithms and techniques otherthan those described can be used.

The terminology used in this application with respect to modifying asignal is meant to include all devices and environments in which asignal can be modified. Therefore, it is manifestly intended that thisinvention be limited only by the following claims and equivalentsthereof.

What is claimed is:
 1. A computerized method comprising: analyzing avideo input signal to detect image content and image properties, whereindetecting the image content includes automatically deriving imagefeatures; determining a content group for the video input signal basedon a directional blur of a motion of an object in the detected imagecontent, the object occupying a portion of the detected image content,the content group including predefined image properties and the contentgroup represents a type of image composition captured in the video inputsignal; and adjusting at least one of image display settings and thevideo input signal based on a difference between the detected imageproperties and the predefined image properties.
 2. The computerizedmethod of claim 1, wherein the video input signal is further analyzed todetect signal properties, and wherein the content group is determinedbased on the detected image content and the signal properties.
 3. Thecomputerized method of claim 2, wherein the signal properties comprisemetadata that provides information about at least one of the imagecontent and the image properties, and wherein the content group isdetermined using the metadata.
 4. The computerized method of claim 1,wherein the detected image content includes a first image portion havingfirst image properties and a second image portion having second imageproperties, further comprising: determining a first content group forthe first image portion, the first content group including firstpredefined image properties; determining a second content group for thesecond image portion, the second content group including secondpredefined image properties; adjusting at least one of the image displaysettings and the video input signal for the first image portion based ona difference between the first image properties and the first predefinedimage properties; and adjusting at least one of the image displaysettings and the video input signal for the second image portion basedon a difference between the second image properties and the secondpredefined image properties.
 5. The computerized method of claim 1,further comprising: receiving current image display settings of adisplay; and outputting an output signal to the display that, when shownon the display using the received image display settings, will have thepredefined image properties.
 6. The computerized method of claim 1,further comprising: receiving the video input signal.
 7. Thecomputerized method of claim 1, wherein the video input signal includesdetected audio properties and the content group includes predefinedaudio properties, further comprising: adjusting audio settings based ona difference between the detected audio properties and the predefinedaudio properties.
 8. A non-transitory machine-readable medium includinginstructions that, when executed by a machine having a processor, causethe processor to perform a computerized method comprising: analyzing avideo input signal to detect image content and image properties, whereindetecting the image content includes automatically deriving imagefeatures; determining a content group for the video input signal basedon a directional blur of a motion of an object in the detected imagecontent, the object occupying a portion of the detected image content,the content group including predefined image properties and the contentgroup represents a type of image composition captured in the video inputsignal; and adjusting at least one of image display settings and thevideo input signal based on a difference between the detected imageproperties and the predefined image properties.
 9. The non-transitorymachine-readable medium of claim 8, wherein the video input signal isfurther analyzed to detect signal properties, and wherein the contentgroup is determined based on the detected image content and the signalproperties.
 10. The non-transitory machine-readable medium of claim 9,wherein the signal properties comprise metadata that providesinformation about at least one of the image content and the imageproperties, and wherein the content group is determined using themetadata.
 11. The non-transitory machine-readable medium of claim 8,wherein the detected image content includes a first image portion havingfirst image properties and a second image portion having second imageproperties, the computerized method further comprising: determining afirst content group for the first image portion, the first content groupincluding first predefined image properties; determining a secondcontent group for the second image portion, the second content groupincluding second predefined image properties; adjusting at least one ofthe image display settings and the video input signal for the firstimage portion based on a difference between the first image propertiesand the first predefined image properties; and adjusting at least one ofthe image display settings and the video input signal for the secondimage portion based on a difference between the second image propertiesand the second predefined image properties.
 12. The non-transitorymachine-readable medium of claim 8, the computerized method furthercomprising: receiving current image display settings of a display; andoutputting an output signal to the display that, when shown on thedisplay using the received image display settings, will have thepredefined image properties.
 13. The non-transitory machine-readablemedium of claim 8, wherein the input video signal includes metadata thatprovides information about at least one of the image content and theimage properties, and wherein the content group is determined using themetadata.
 14. The non-transitory machine-readable medium of claim 8,wherein the video input signal includes detected audio properties andthe content group includes predefined audio properties, the computerizedmethod further comprising: adjusting audio settings based on adifference between the detected audio properties and the predefinedaudio properties.
 15. The non-transitory machine-readable medium ofclaim 14, wherein adjusting the audio properties comprises at least oneof adjusting volume, adjusting equalization, and adjusting audio speed.16. An apparatus comprising: means for analyzing a video input signal todetect image content and image properties, wherein detecting the imagecontent includes automatically deriving image features; means fordetermining a content group for the video input signal based on adirectional blur of a motion of an object in the detected image content,the object occupying a portion of the detected image content, thecontent group including predefined image properties and the contentgroup represents a type of image composition captured in the video inputsignal; and means for adjusting at least one of image display settingsand the video input signal based on a difference between the detectedimage properties and the predefined image properties.
 17. The apparatusof claim 16, wherein: the means for analyzing the video input signalfurther to detect signal properties; and means for determining thecontent group based on the detected image content and the signalproperties.
 18. The apparatus of claim 16, wherein the detected imagecontent includes a first image portion having first image properties anda second image portion having second image properties, furthercomprising: means for determining a first content group for the firstimage portion, the first content group including first predefined imageproperties; means for determining a second content group for the secondimage portion, the second content group including second predefinedimage properties; means for adjusting at least one of the image displaysettings and the video input signal for the first image portion based ona difference between the first image properties and the first predefinedimage properties; and means for adjusting at least one of the imagedisplay settings and the video input signal for the second image portionbased on a difference between the second image properties and the secondpredefined image properties.
 19. The apparatus of claim 16, furthercomprising: means for receiving current image display settings of adisplay; and means for outputting an output signal to the display that,when shown on the display using the received image display settings,will have the predefined image properties.
 20. The apparatus of claim16, wherein the video input signal includes detected audio propertiesand the content group includes predefined audio properties, furthercomprising: adjusting audio settings based on a difference between thedetected audio properties and the predefined audio properties.
 21. Acomputerized system comprising: a processor; a memory coupled to theprocessor though a bus; an analyzer process executed from the memory bythe processor to cause the processor to analyze a video input signal todetect image content and image properties, wherein detecting the imagecontent includes automatically deriving image features; a groupdeterminer process executed from the memory by the processor to causethe processor to determine a content group for the video input signalbased on a directional blur of a motion of an object in the detectedimage content, the object occupying a portion of the detected imagecontent, the content group including predefined image properties and thecontent group represents a type of image composition captured in thevideo input signal; and an adjustor process executed from the memory bythe processor to cause the processor to adjust at least one of imagedisplay settings and the video input signal based on a differencebetween the detected image properties and the predefined imageproperties.
 22. The computerized method of claim 1, wherein the contentgroup is selected from the group consisting of a portrait group, alandscape group, a sports group, a cinema content group, a text contentgroup, a still picture content group, a concert content group, and ahome video content group.
 23. The computerized method of claim 1,wherein the determining the content group is further based on adistribution of colors in the video input signal.